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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-22k-224 |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: convnextv2-tiny-22k-224-finetuned-galaxy10-decals |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# convnextv2-tiny-22k-224-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-224](https://huggingface.co/facebook/convnextv2-tiny-22k-224) on the matthieulel/galaxy10_decals dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4373 |
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- Accuracy: 0.8636 |
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- Precision: 0.8625 |
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- Recall: 0.8636 |
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- F1: 0.8603 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.5665 | 0.99 | 62 | 1.3996 | 0.5287 | 0.5180 | 0.5287 | 0.4897 | |
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| 0.8598 | 2.0 | 125 | 0.7433 | 0.7463 | 0.7490 | 0.7463 | 0.7396 | |
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| 0.7163 | 2.99 | 187 | 0.5703 | 0.7948 | 0.7919 | 0.7948 | 0.7863 | |
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| 0.5858 | 4.0 | 250 | 0.5194 | 0.8269 | 0.8292 | 0.8269 | 0.8190 | |
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| 0.5382 | 4.99 | 312 | 0.4936 | 0.8309 | 0.8314 | 0.8309 | 0.8302 | |
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| 0.5546 | 6.0 | 375 | 0.5054 | 0.8292 | 0.8366 | 0.8292 | 0.8234 | |
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| 0.5067 | 6.99 | 437 | 0.4817 | 0.8281 | 0.8324 | 0.8281 | 0.8278 | |
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| 0.4617 | 8.0 | 500 | 0.4565 | 0.8501 | 0.8545 | 0.8501 | 0.8497 | |
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| 0.4619 | 8.99 | 562 | 0.4382 | 0.8534 | 0.8520 | 0.8534 | 0.8498 | |
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| 0.4416 | 10.0 | 625 | 0.4330 | 0.8529 | 0.8505 | 0.8529 | 0.8504 | |
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| 0.4267 | 10.99 | 687 | 0.4274 | 0.8574 | 0.8575 | 0.8574 | 0.8566 | |
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| 0.3919 | 12.0 | 750 | 0.4407 | 0.8585 | 0.8604 | 0.8585 | 0.8563 | |
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| 0.3929 | 12.99 | 812 | 0.4373 | 0.8636 | 0.8625 | 0.8636 | 0.8603 | |
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| 0.3989 | 14.0 | 875 | 0.4351 | 0.8585 | 0.8602 | 0.8585 | 0.8577 | |
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| 0.3426 | 14.99 | 937 | 0.4476 | 0.8495 | 0.8500 | 0.8495 | 0.8484 | |
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| 0.361 | 16.0 | 1000 | 0.4463 | 0.8517 | 0.8505 | 0.8517 | 0.8501 | |
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| 0.2996 | 16.99 | 1062 | 0.4694 | 0.8596 | 0.8604 | 0.8596 | 0.8579 | |
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| 0.3394 | 18.0 | 1125 | 0.4494 | 0.8523 | 0.8526 | 0.8523 | 0.8517 | |
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| 0.3207 | 18.99 | 1187 | 0.4863 | 0.8506 | 0.8502 | 0.8506 | 0.8496 | |
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| 0.2993 | 20.0 | 1250 | 0.4748 | 0.8551 | 0.8516 | 0.8551 | 0.8521 | |
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| 0.287 | 20.99 | 1312 | 0.4980 | 0.8467 | 0.8436 | 0.8467 | 0.8434 | |
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| 0.3331 | 22.0 | 1375 | 0.4829 | 0.8546 | 0.8530 | 0.8546 | 0.8519 | |
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| 0.2852 | 22.99 | 1437 | 0.4943 | 0.8512 | 0.8520 | 0.8512 | 0.8508 | |
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| 0.2813 | 24.0 | 1500 | 0.4796 | 0.8574 | 0.8574 | 0.8574 | 0.8568 | |
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| 0.2807 | 24.99 | 1562 | 0.4811 | 0.8596 | 0.8576 | 0.8596 | 0.8576 | |
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| 0.2609 | 26.0 | 1625 | 0.4786 | 0.8608 | 0.8589 | 0.8608 | 0.8592 | |
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| 0.2571 | 26.99 | 1687 | 0.4777 | 0.8608 | 0.8605 | 0.8608 | 0.8602 | |
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| 0.2807 | 28.0 | 1750 | 0.4879 | 0.8596 | 0.8580 | 0.8596 | 0.8582 | |
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| 0.2578 | 28.99 | 1812 | 0.4829 | 0.8557 | 0.8550 | 0.8557 | 0.8549 | |
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| 0.2543 | 29.76 | 1860 | 0.4833 | 0.8563 | 0.8555 | 0.8563 | 0.8554 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.1 |
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